Abstract

Fatigue is a common symptom in Western high-income countries but is often
medically unexplained and little is known about its presentation in other
populations.

Aims

To explore the epidemiology and aetiology of fatigue in Sri Lanka, and of
its overlap with depression.

Method

A total of 4024 randomly selected twins from a population-based register in
Sri Lanka (Colombo district) completed home interviews including the Chalder
Fatigue Questionnaire.

Results

The prevalence of fatigue was similar to that in other countries, although
prolonged fatigue may be less common. There was substantial comorbidity with a
screen for lifetime depression. Non-shared environmental factors made the
largest contributions, although genetic/family factors also contributed. The
aetiology appeared consistent across the spectrum of severity.

Conclusions

The aetiology of fatigue is broadly similar in Sri Lanka and Western
high-income countries. Abnormal experiences of fatigue appear to be the
extreme form of more common fatigue, rather than representing independent
entities with different genetic or environmental risk factors.

Most fatigue research has been conducted in high-income Western countries
despite a greater rate of fatigue presentations to health services in low- and
middle-income
countries.1 Most
studies of symptomatic fatigue and chronic fatigue (i.e. present for at least
6 months) find that, although a small proportion of cases can be accounted for
by defined biomedical disease (e.g. cancer, rheumatoid arthritis) or
treatments, the majority of cases are ‘medically
unexplained’.2,3
In Europe and the USA, about a third of people experience troublesome
fatigue,2,4
5–12% chronic
fatigue2,5,6
and 0.5–2% chronic fatigue
syndrome.7 Twin
studies are one method by which the overall aetiological contributions to
fatigue can be investigated; in European-derived populations twin studies have
suggested a substantial role for genes but a larger contribution from
environmental
factors.8–10
Fatigue falls on a continuum without notable
cut-points,11 which
suggests that studying broader definitions could be informative about more
severe and prolonged cases. Most clinical and epidemiological
studies4,12,13
indicate that risk factors are similar across the spectrum of severity. This
suggests that more severe forms are quantitatively, but not qualitatively,
different from milder fatigue; however, this has not been tested using a
genetically sensitive method. Despite the potential heterogeneity within
fatigue, and although fatigue symptoms can be separated from mood symptoms,
there are striking associations between fatigue or chronic fatigue syndrome
and psychiatric symptoms or disorders in the general
population.8,14,15
Although the aetiology of this overlap is poorly understood, evidence from
birth cohorts indicates that psychiatric disorder may often predispose
individuals to fatigue or chronic fatigue
syndrome15 and twin
studies implicate genetic pleiotropy across
symptoms.8,9
Thus fatigue is an important, prevalent, disabling but poorly understood
phenomenon. The aim of the current study was to investigate whether these
patterns of findings are the same in a non-Western low-income country, Sri
Lanka, where the important environmental impacts and cultural contexts might
be expected to be considerably different to those previously studied.

Method

The study received approval from the Institute of Psychiatry, King’s
College London Research Ethics Committee; the Ethical Review Committee,
University of Sri Jayewardanepura; and the World Health Organization’s
(WHO’s) Research Ethics Committee.

Study design and participants

The study aimed to examine the prevalence and aetiology of fatigue in Sri
Lanka, and of its overlap with depressive episodes. In addition, it aimed to
clarify the relationship between the range of normal variation and more
pronounced fatigue.

This was a population-based twin study, the twin component of the Colombo
Twin And Singleton Study (CoTASS). Full details of the design and
implementation of the study are described
elsewhere.16
Briefly, the study took place in the Colombo District of Sri Lanka, an area
with a population of 2.2 million that includes the island’s capital and
urban to semi-urban areas. We added a question to the update of the annual
census asking whether the householder knew of any twins and identified 19 302
individual twins by this method. Of these, we randomly selected 4387
individual twins who were at least 15 years old to take part in the project on
common mental disorders. A total of 4024 (91.7%) participated, including 1954
complete twin pairs. We included all consenting individuals aged 15 years or
older who spoke sufficient Sinhala to understand the interview, excluding
those who failed a mini-mental state examination and those whose data was
provided by a proxy. The mean age of the twins was 34.0 years, 46.2% were male
and 90.1% were of Sinhalese ethnicity. High-school-educated research workers
visited the participants’ homes to interview them each separately.
Interviews took place between 2006 and 2007 when Sri Lanka had been
experiencing violent civil war for over 20 years. However, much of the
conflict has centred in areas to the North and East of the island, far from
the location of the current study. Nonetheless, a small minority (2.6%) of the
participants reported directly participating in the conflict as
combatants.

Measures

All interviews and questionnaires were translated at least twice
independently into Sinhala, and were then reviewed by a group of professionals
and a scholar in Sinhala, and finally trialled to confirm lay people could
understand it.16
All questionnaires were administered as interviews.

Participants were assessed with the Chalder Fatigue
Questionnaire.17
This includes 11 core fatigue items, and 2 assessing muscle pain, experienced
over the past month (scored as: less than usual, no more than usual, more than
usual or much more than usual, which were coded as 0, 0, 1, 1 for categorical
analysis and 0, 1, 2, 3 for continuous analysis). Those who had had fatigue
for a long time were asked to rate their past month’s experiences
compared with when they last felt well. It also assesses what percentage of
the time fatigue is present, and among those who are currently fatigued, it
assesses how long this has lasted. We created three variables to analyse
fatigue both as a continuum and as dichotomies indicating potentially
clinically significant fatigue. ‘Fatigue severity’ is a continuous
scale using all 13 symptom items each scored 0–3. For genetic analyses,
this scale was log-transformed to remove skew, and the mean effects of age and
gender were regressed out (in order to avoid artificially inflated estimates
of shared environmental factors). ‘Abnormal fatigue’ indicates
that 3 of the 11 fatigue items were present at least ‘more than
usual’; ‘prolonged fatigue’ indicates that at least 4 of the
11 fatigue items were present at least ‘more than usual’ and for
at least 50% of the time and for at least 6 months.

A dimensional approach to measurement can be useful especially where there
appears to be no ‘point of rarity’ marking a discrete disorder and
where there may be a complex combination of risk factors involved in
aetiology, which is unlikely to be adequately indexed by a single diagnostic
category.18 It is
also appropriate where there is no clear Mendelian pattern of inheritance,
suggesting that any genetic effect may come from many separate loci, each of
small effect size, and each of which may contribute to the range of normal
variation as well as differentiating cases from controls (i.e. the
quantitative trait loci
hypothesis).19 The
current study used a mix of dimensional and categorical definitions of fatigue
in order to test the consistency of the findings across different severity
levels. Prior population-based twin studies have only examined approximations
of chronic fatigue syndrome rather than the official definition, partly
because of the large sample sizes required for twin analysis of uncommon
conditions in representative populations. The current study also takes this
approach, but explicitly tests the validity of making comparisons between
broader and narrower measures of fatigue. It is also the first genetically
informative exploration of fatigue symptoms outside of the Western high-income
world.

All participants were interviewed with the Composite International
Diagnostic
Interview.20 This
includes two ‘probe’ questions relating to lifetime ever
depression (which are the two core symptoms of depression according to
DSM–IV/ICD–10).21,22
These are low mood and loss of interest in things normally enjoyed for at
least a 2-week period (thus neither item tapped fatigue as a symptom within
the depressive episode). We used a positive score on either of these as a
screen for lifetime ever depression, ‘D-probe’, because this
relatively broad definition is suitable for twin analysis especially in the
context of broad definitions of fatigue and because it avoids potential
cultural biases in the assessment of impairment.

An item from the Short Form–36 was used to indicate social/functional
impairment: ‘During the past 4 weeks, how much of the time has your
physical health or emotional problems interfered with social activities (like
visiting friends, relatives
etc)?’23 This
item was scored on a Likert scale but was collapsed into a binary (yes/no)
score. Zygosity was assessed using a validated
questionnaire24,25
administered to both twins.

Analyses

A database was constructed, and correlational and extremes analyses were
performed in Stata version 10.1 for Windows. Analyses were corrected for
non-independence within pairs where appropriate. Genetic model fitting was
performed in Mx for Windows
(www.vcu.edu/mx/index.html).

The twin analyses involve calculating the proportion of individual
differences in a given trait that can be detected as a result of additive
genetics (A), environments shared between twins (C) and
environments unique to each twin in a pair (E). The size of these
parameters is estimated based on the knowledge that monozygotic (MZ) pairs of
twins share 100% of their genetic material, whereas dizygotic (DZ) pairs share
on average 50% of their genetic material, relative to the rest of the
population. By definition, both MZ and DZ twin pairs share 100% of their
shared environments and 0% of their non-shared environments.

Extremes analyses (comparing extreme groups with normal
variation)

In order to test the hypothesis that the aetiology of fatigue is the same
at the extreme as in the range of normal variation, we ran
DeFries–Fulker extremes regression
models.26 This
method typically requires a continuously measured trait, onto which a
cut-point is made in order to create a dichotomy to identify probands. The
co-twin mean score is compared with the proband mean and the population mean.
In this study we used fatigue severity as the continuous measure and abnormal
fatigue as the categorical variable to define probands. We are in essence
conducting a bivariate analysis to describe the relationship between an
extreme dichotomy and the range of fatigue reported in the population measured
as a continuous
trait.27 To
increase power, we compared both male and female MZ pairs to male, female and
opposite gender DZ pairs (this approach is supported by a lack of gender
differences in the univariate models below). The regression models allow
calculation of group heritability (gA) and group
environmentality (gC and
gE). These indicate the extent to which A,
C and E factors are responsible for the probands having a higher
mean than the rest of the population. Evidence of
gA and/or gC
indicates that some of the A or C factors that influence the
extreme also influence the normal distribution.

In more detail, the fatigue severity mean scores of the co-twins of the
probands are standardised on the proband mean scores. This is referred to as
the group co-twin correlation (or transformed co-twin mean) because it focuses
on the mean score of the co-twins rather than individual
differences.27 The
degree of regression of this transformed co-twin mean to the population mean
is examined. If there are genetic factors that influence the extreme as well
as normal variation of fatigue, we would expect the MZ co-twins to regress
less towards the mean than DZ co-twins.

Twin variance component models (individual differences)

Univariate genetic models decompose the variance in each trait into that
resulting from A, C and E (i.e. the variation between
individuals is divided into that accountable by genetic, shared environmental
and non-shared environmental factors). The estimated model is compared with
the observed data in order to produce the maximum likelihood fit of the model.
This model fit is compared with that of a fully saturated model. Separate
thresholds or means were estimated according to gender. Univariate genetic
models were performed for the continuously measured fatigue severity and
categorical abnormal fatigue. Twin variance components models using
categorical data assume a liability threshold model (for more details, see
footnotes to Tables 4 and
5).

A bivariate categorical genetic model was used to decompose the covariance
between abnormal fatigue and D-probe into A, C and E
influences. Due to the gender differences in the heritability of D-probe
reported in a previous
study,28 the
bivariate model was run to examine males and females separately
(opposite-gender pairs were not included). A Cholesky model was fitted, in
which the latent A, C and E factors for the first variable
(abnormal fatigue) are also allowed to load onto the second variable
(D-probe). There is also a separate set of A, C and E
factors that load only on the second variable (D-probe). However, for ease of
interpretation, we will present the mathematically equivalent correlated
factors solution. Here, separate A, C and E parameters are
estimated for each variable, and the extent that the phenotypic overlap is a
result of A, C or E is calculated
(Fig. 1).

Only one twin from each pair is shown. The double-headed arrows represent
the correlations between latent factors (e.g. r, the correlation between the
genetic influence (A) on fatigue and the genetic influence on depression). The
genetic contribution to the overall phenotypic overlap is found by examining
the size of three paths: the genetic contribution to fatigue, r, and the
genetic contribution to depression. This is also calculated for C (shared
environment) and E (non-shared environment). Since the total phenotypic
correlation consists of contributions from A, C and E, percentage
contributions can then be calculated.

Results

Table 1 shows that men had
less fatigue than women, both when measured as a continuous variable
(t = 5.48, P<0.001) and as a categorical variable,
presence/absence of abnormal fatigue (z = 5.11, P<0.001).
The prevalence of prolonged fatigue was low, giving low power to detect gender
differences.

Low prevalence of prolonged fatigue

To assess the duration of fatigue, participants were asked ‘If you
are tired at the moment, please indicate approximately how long this has
lasted’. In total, 72% of those who reported sufficient specific fatigue
symptoms over the past month for the most extreme definition denied
experiencing fatigue at the moment of the interview when asked about its
duration (for comparison, in a random sample of UK armed forces personnel
using the same
questionnaire29
this figure was roughly a quarter). This subset, who did admit to substantial
generalised fatigue over the past month (only 20% claimed to be not tired in
response to the question: ‘Overall, what percentage of the time do you
feel tired’), were thus ineligible for a duration score, and this
apparent disparity partially accounts for the low prevalence of prolonged
fatigue.

The data were double checked, and the translation of the relevant items was
confirmed to be accurate and to reflect the same meaning as the English
version, so there is a high level of confidence in the validity of these
responses. The disparity may instead relate to the context of the study
population (e.g. what is viewed as an abnormal level of tiredness in Sri
Lanka), in which case the individuals meeting criteria for prolonged fatigue
may be representing a more severe (and thus possibly more reliable and
informative) subset of the phenotype than those picked up under similar
criteria in other countries. The disparity may also have been triggered by the
administration of the questionnaire as a face-to-face interview, whereas in
other settings it has usually been completed by the participant alone, using
pen and paper. Alternatively, this result may suggest that although fatigue is
a common condition in Sri Lanka, prolonged fatigue is considerably rarer. Thus
the responses are valid in respect of the items asked, but precise wording of
the questions (i.e. reference to the specific moment of the interview) may
have influenced the prevalence. Because of this uncertainty, the prolonged
fatigue category was used as a supplementary category for comparison rather
than a focus of the analyses in this paper.

Impairment

Reporting of social impairment over the past month was higher among those
who either reported fatigue over the past month or 2 weeks of depressive
symptoms over their lifetime (i.e. D-probe)
(Table 2). Impairment was
significantly more common among those reporting both abnormal fatigue and
lifetime depressive symptoms compared with abnormal fatigue only (Wald test of
equality of odds ratios: χ2 = 14.2, P <0.001), but
this fell short of significance when repeated using prolonged fatigue
(χ2 = 2.1, P = 0.14). So, having both abnormal fatigue
and D-probe is a marker of heightened impairment, which makes it particularly
interesting to explore the aetiological factors explaining why some people
report both, as we have done in the bivariate model reported at the end of the
results section.

Social impairment among participants with fatigue and/or a screen for
lifetime depressive episodes (D-probe)

Extremes analysis

The Defries–Fulker extremes analysis using abnormal fatigue as the
cut-off (and fatigue severity to index normal variation) suggested an
important influence of group heritability but not group-shared
environmentality (gA = 24%, P = 0.026;
gC = 4%, P = 0.309; n = 774
pairs of twins selected for analysis, i.e. with at least one twin with
abnormal fatigue). This indicates that there is aetiological overlap between
the extreme and normal variation.

Correlations

In order to examine the heritability of the different definitions of
fatigue, we first examined the cross-twin correlations for fatigue severity
and abnormal fatigue (Table 3).
We did not examine cross-twin correlations or variance decomposition models
for the narrower definition of prolonged fatigue because of low prevalence.
The MZ correlations were slightly greater than the DZ correlations among men,
whereas the correlations were more similar across zygosity for women, perhaps
indicating greater genetic influence in men and greater shared environmental
influences in women. However, the confidence intervals around these estimates
are wide. None of the correlations were greater than 0.48 indicating a
considerable role for non-shared environmental factors.

Cross-twin and within-person correlations (95% CIs) for fatigue and a
screen for lifetime depressive episodes (D-probe)a

A history of D-probe (the screen for lifetime depressive episodes) was
associated with abnormal fatigue (odds ratio (OR) = 3.19, 95% CI
2.64–3.84) and prolonged fatigue (OR = 6.47, 95% CI 3.36–12.47),
when looking within individuals but controlling for familial relatedness. All
three of the fatigue indices correlated significantly with D-probe; the
magnitude of this correlation was slightly less when assessing the full range
of fatigue variation (fatigue severity) than the two categorical indices
(Table 3). This is consistent
with research from a WHO study in 14
countries30 that
showed a stronger relationship between depression and (unexplained) fatigue
syndromes with narrower definitions of fatigue. The bivariate cross-twin
correlations suggest genetic factors are involved in this association for
women only (because the MZ cross-trait correlation is roughly double that for
DZs, Table 3). However, the
confidence intervals are wide, and the relatively low magnitude of the MZ male
bivariate correlation suggests a large influence from non-shared environmental
factors.

Univariate twin models

Fatigue severity had a larger standard deviation in women than in men (0.17
and 0.14 respectively, F = 0.704, P<0.001), so we ran a
scalar variance components model to the saturated model, which fit well
(Δχ2 = 10.06, d.f. = 9, P = 0.346). The variance
components models for abnormal fatigue also fit the respective saturated model
well (Δχ2 = 0.34, d.f. = 1, P = 0.560). For both
definitions, there was no evidence of qualitative gender differences
(P-values >0.56). There were no quantitative gender differences
(there was a significant familial influence in both genders, although men
tended to have higher A and lower C estimates, and this was
marginally significant for abnormal fatigue (P = 0.058)). We
proceeded using models with ACE parameters equated for men and women
(Table 4).

The ACE parameter estimates were similar across the two
definitions. The largest contribution to the variance came from the non-shared
environment (72% in fatigue severity and 62% in abnormal fatigue), followed by
additive genetics (21% and 29%) and with minimal estimated contributions from
the shared environment (7% and 8%). These values are very similar to the group
heritability and group-shared environmentality obtained in the extremes
analyses. The effect of additive genetics was significant in the fatigue
severity model (P = 0.017) and was marginally significant for
abnormal fatigue (P = 0.065). Neither of the shared environmental
parameters were significantly greater than zero (P-values >0.30).
The data are best explained by the AE models (i.e. additive genetic
plus non-shared environmental effects).

It should be noted that statistical power to detect gender differences was
low, particularly for the category of abnormal fatigue. This is partly because
the large magnitude of the E parameter makes it hard to distinguish
the relative contributions of (or possibly the mix of) A and
C. Therefore these results only provide tentative evidence of lack of
gender differences, although they are useful in telling us that an AE
model best explains the data for men and women combined. Confidence in the
abnormal fatigue univariate results is improved as a result of the similar
magnitude of aetiological effects identified both with the continuously
measured fatigue severity, and the extremes analyses examining the overlap
between fatigue severity and abnormal fatigue.

Bivariate twin model

Finally, we ran a bivariate model to decompose the covariation between
abnormal fatigue and D-probe (see Table
5 for parameter estimates). As a result of previously identified
gender differences in
D-probe,28 we did
not attempt to equate the model across genders. The only significant
individual contribution to the overlap in men was from E. In women,
none of the three parameters were individually significant using a χ
2-test, although E was the most likely contributor
based on Akaike’s information criterion (AIC). However, although we
could not distinguish between A and C, there was significant
familial influence (combination of A and C) in both men
(Δχ2 = 7.03, d.f. = 2, P = 0.03, AIC = 3.03) and
women (Δχ2 = 24.29, d.f. = 2, P<0.001, AIC =
20.29). So the overlap between fatigue and history of depressive symptoms is
associated with high levels of impairment, and this overlap is explained by a
combination of person-specific environments and familial factors.

Discussion

The aims of this study were to examine the characteristics and determinants
of fatigue in a population-representative sample from outside Western
high-income countries; and to describe the relationship between the normal
range of fatigue and more extreme presentations. Key findings were that
troublesome fatigue was common in Sri Lanka with a prevalence of 25%, but
prolonged fatigue was considerably less common (roughly 1%). The prevalence of
troublesome fatigue is in line with findings in other
countries,2,4
but prolonged fatigue in Sri Lanka was lower than in many Western high-income
countries;2,5,6
however, this may have been influenced by different interpretations of the
precise wording of the item assessing duration.

We found the largest aetiological influence came from non-shared
environmental factors and familial similarity in fatigue appears to be a
result of shared genes rather than shared environment. An aetiological overlap
was identified between the determinants of variation in the normal range of
fatigue and in more extreme cases. This means that the risk and protective
factors contributing to the normal range of fatigue also contribute to more
severe and prolonged fatigue.

Fatigue was associated with a positive screen for past depressive episodes,
and both contributed to impairment in functioning. Impairment was especially
marked among people affected by both, making it particularly pertinent to
understand the reasons behind this overlap. In other countries, genetic or
familial factors have been found to be responsible for the overlap between
fatigue and mental
distress.8,9,32
Although familial factors did contribute in Sri Lanka, we also found
influences from non-shared environmental factors.

Low prevalence of prolonged fatigue

The low prevalence of fatigue in Sri Lanka compared with estimates from
high-income Western countries mirror findings from a WHO multinational study,
which found that fatigue assessed via direct questioning was less prevalent in
low-compared with high-income
countries.1 The
prevalence in Sri Lanka was particularly low for prolonged fatigue (which
required the participant to respond positively to further questioning,
including being fatigued at the time of the interview). This could be a true
difference in underlying prevalence, suggesting a difference in the identity
or prevalence of some of the risk factors, or an effect of the wider social or
physical environment buffering the effect of specific risk factors.
Alternatively, the low prevalence may reflect a tendency for participants to
play down symptoms upon further questioning (Sri Lankan participants might be
more likely to view fatigue as a fact of life rather than something unusual to
be commented on or investigated). The WHO study conversely found more
spontaneous fatigue presentations in low-compared with high-income
countries1 and a
study in Goa,
India,33 found
rates comparable with high-income countries using questions from the Revised
Clinical Interview Schedule (CIS–R). These patterns suggest that the
precise wording of questions is important and interpretation of the questions
may differ across countries. However, the initial items on the Chalder Fatigue
Questionnaire used in this study (to generate the fatigue severity and
abnormal fatigue classifications) may be particularly useful, because they
specifically enquire about the past month’s symptoms in relation to what
is usual for the participant (i.e. compared with the previous month, except
where the participant has been fatigued for a long time, in which case the
comparison is with when they last felt well).

Aetiology

The overall aetiology of fatigue was similar to that in high-income Western
countries,8–10
although power constraints make it hard to be precise. This is noteworthy
given the different profile of communicable versus non-communicable disease in
Sri Lanka, which might be expected to lead to a different aetiological profile
than that seen in high-income Western countries, if fatigue is typically a
consequence of medical conditions each with differing risk factors.
Nonetheless, the results did suggest a small contribution from genetic
factors, in keeping with genetically informative research on chronic fatigue
syndrome that has suggested larger genetic influences on objective domains
(e.g. sleep latency, cold pain threshold and tolerance) than subjective
domains (e.g. reports of fatigue or
pain).34 There was
evidence that suggested that environmental influences on the overlap between
fatigue and an indicator of depression were higher in Sri Lanka than in
high-income Western countries. This is a tentative finding because of the
uncertainty in the estimates of the bivariate analyses, and may be partly as a
result of low power (we could not rule out an overall familial effect).
Alternatively, it might reflect differences in measurement, or possibly more
varied experiences of actual environmental factors influencing both fatigue
and mental health in Sri Lanka. A previous study on the same
sample35 has shown
how factors including poverty, housing and working conditions are related to
depression in Sri Lanka, especially among men (a corresponding gender
difference was not found in relation to fatigue in the current report, except
when examining the overlap with depression).

Although non-shared environmental influences (E) strongly
influence fatigue both in Sri Lanka and in other countries, the precise nature
of the exposure may differ. E could include each twin’s
separate occupational and social activities and thus their differential
exposure to stressors and infections over the past month or beyond, where
these exposures would lead to cross-twin differences in tiredness. Some
research has linked chronic fatigue syndrome to infections such as the
Epstein–Barr
virus.36,37
However, other research has downplayed a link to
infections,38 or
else suggests that individual responses to infection such as immune activation
or prolonged convalescence are
important,39,40
particularly when considering (in relation to Epstein–Barr virus) the
high proportion of cases that do not go on to develop chronic fatigue
syndrome. More longstanding influences could also be important, e.g. lifestyle
factors such as childhood over-exercise (which is a prospective risk
factor)41 or
childhood trauma which is linked to psychopathology as well as chronic fatigue
syndrome42 –
such experiences often differ for each twin in a pair, or are perceived
differently by each twin. The E influences in the univariate models
also incorporate measurement error.

The finding that the aetiology of fatigue is similar for the narrow and
broad definitions (i.e. abnormal fatigue and fatigue severity) concurs with
data from over 30 000 Swedish twins. The Swedish study compared the 95%
confidence intervals for varying definitions of fatigue (including chronic
fatigue syndrome-like illness assessed using medical exclusions), and showed
that the A/C/E proportions did not change according
to the stringency of the
definition.10 The
current study adds that the genetic and environmental risk factors that
contribute to excessive and prolonged experiences of fatigue (abnormal fatigue
and prolonged fatigue) also appear to influence the normal range of fatigue
(fatigue severity), and that the different definitions were all associated
with a screen for history of depressive episodes. This suggests that, although
fatigue may have non-psychiatric medical causes, the links with psychiatric
morbidity are substantial and may be more important than environmental
exposures or disease burden on a population level.

We found no evidence of gender differences in the aetiology of fatigue,
which is interesting in the light of the relative status of men and women in
Sri Lanka. The gender discrepancy experienced in everyday life in Sri Lanka
may be more pronounced than in many Western and/or higher-income countries,
but in certain aspects it is less pronounced than other countries of the
Indian subcontinent, for example gender equality has already been reached in
Sri Lanka in primary and secondary
education.43

Limitations

The current study assessed fatigue over the past month (and for prolonged
fatigue, whether this had been present for the past 6 months). This might have
led to underestimates of true heritability because of the possibility of twins
being discordant over the month but concordant over their lifetimes. However,
these analyses are useful for the purposes of making comparisons with data
from other countries that used similar methods.

The specificity of our findings are limited because we did not use any
medical exclusions. However, previous results from Sweden found that the
aetiology of fatigue was similar regardless of the stringency of the
definition used, or the use of medical
exclusions.10
Moreover, this is a relatively young population-based
sample,16 few of
whom are likely to be affected by medically diagnosable physical disorders. If
there were a range of undetected medical diagnoses that contributed to
reporting of fatigue, this is likely to have increased measurement error and
so the calculated heritability could be an underestimate.

The precise wording of the fatigue duration item may have influenced the
prevalence of the ‘prolonged fatigue’ category, so it is not clear
whether this reflects a true difference in the rate of extended experiences of
fatigue in Sri Lanka. However, the other two definitions are much more likely
to have been interpreted in a consistent manner across countries because they
focus on recent experiences in comparison with what is usual for the
participant.

The assumptions of the twin method apply to these findings. For example, it
is assumed that MZ pairs are not treated any more similarly than DZ pairs
purely on the basis of zygosity or that such differential treatment is not
relevant for the phenotypes under investigation. Such assumptions have been
supported through tests in relation to psychiatric
disorders.44

Conclusions

Fatigue in Sri Lanka is, as in Western countries, common and strongly
associated with depressive symptoms. There is evidence from our work that both
fatigue as a spectrum and abnormal fatigue are mainly determined by non-shared
environmental influences, although there is a modest genetic component, and
that the aetiology is consistent across the spectrum of severity.

Funding

The Wellcome Trust
provided funding for the CoTASS study, and the Institute for Research and
Development, Sri Lanka, provided infrastructural support. H.A.B. is supported
by an ESRC research studentship. M.H. is funded
by the South London and Maudsley NHS Foundation
Trust and Institute of Psychiatry, King’s
College London, National Institute of Health
Research, Biomedical Research Centre.

References

Meltzer H, Gill B, Petticrew M. The Prevalence of
Psychiatric Morbidity Among Adults Aged 16–64, Living in Private
Households, in Great Britain. Office of Population Censuses and
Surveys Social Surveys Division, 1994.